higgs benchmark

Project.toml

@@ -16,6 +16,12 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
+[weakdeps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+
+[extensions]
+EvoTreesCUDAExt = "CUDA"
+
 [compat]
 BSON = "0.3"
 CUDA = "3.0, 4.0, 5.0"
@@ -29,9 +35,6 @@ StatsBase = "0.32, 0.33, 0.34"
 Tables = "1.9"
 julia = "1.6"
 
-[extensions]
-EvoTreesCUDAExt = "CUDA"
-
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -43,6 +46,3 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [targets]
 docs = ["Documenter"]
 test = ["CUDA", "DataFrames", "Test", "MLJBase", "MLJTestInterface"]
-
-[weakdeps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

benchmarks/Higgs-logloss.jl

@@ -0,0 +1,93 @@
+using Revise
+using Random
+using CSV
+using DataFrames
+using StatsBase
+using Statistics: mean, std
+using EvoTrees
+using Solage: Connectors
+using AWS: AWSCredentials, AWSConfig, @service
+
+@service S3
+aws_creds = AWSCredentials(ENV["AWS_ACCESS_KEY_ID_JDB"], ENV["AWS_SECRET_ACCESS_KEY_JDB"])
+aws_config = AWSConfig(; creds=aws_creds, region="ca-central-1")
+bucket = "jeremiedb"
+
+path = "share/data/higgs/HIGGS.arrow"
+df_tot = Connectors.read_arrow_aws(path; bucket="jeremiedb", aws_config)
+
+rename!(df_tot, "Column1" => "y")
+feature_names = setdiff(names(df_tot), ["y"])
+target_name = "y"
+
+function percent_rank(x::AbstractVector{T}) where {T}
+    return tiedrank(x) / (length(x) + 1)
+end
+
+transform!(df_tot, feature_names .=> percent_rank .=> feature_names)
+
+dtrain = df_tot[1:end-500_000, :];
+deval = df_tot[end-500_000+1:end, :];
+dtest = df_tot[end-500_000+1:end, :];
+
+config = EvoTreeRegressor(
+    loss=:logloss,
+    nrounds=5000,
+    eta=0.15,
+    nbins=128,
+    max_depth=9,
+    lambda=1.0,
+    gamma=0.0,
+    rowsample=0.8,
+    colsample=0.8,
+    min_weight=1,
+    rng=123,
+)
+
+device = "gpu"
+metric = "logloss"
+@time m_evo = fit_evotree(config, dtrain; target_name, fnames=feature_names, deval, metric, device, early_stopping_rounds=200, print_every_n=100);
+
+p_test = m_evo(dtest);
+@info extrema(p_test)
+logloss_test = mean(-dtest.y .* log.(p_test) .+ (dtest.y .- 1) .* log.(1 .- p_test))
+@info "LogLoss - dtest" logloss_test
+error_test = 1 - mean(round.(Int, p_test) .== dtest.y)
+@info "ERROR - dtest" error_test
+# ┌ Info: LogLoss - dtest
+# └   logloss_test = 0.4716574579097044
+# ┌ Info: ERROR - dtest
+# └   error_test = 0.229522
+
+@info "XGBoost"
+@info "train"
+using XGBoost
+params_xgb = Dict(
+    :num_round => 4000,
+    :max_depth => 8,
+    :eta => 0.15,
+    :objective => "reg:logistic",
+    :print_every_n => 5,
+    :gamma => 0,
+    :lambda => 1,
+    :subsample => 0.8,
+    :colsample_bytree => 0.8,
+    :tree_method => "gpu_hist", # hist/gpu_hist
+    :max_bin => 128,
+)
+
+dtrain_xgb = DMatrix(select(dtrain, feature_names), dtrain.y)
+watchlist = Dict("eval" => DMatrix(select(deval, feature_names), deval.y));
+@time m_xgb = xgboost(dtrain_xgb; watchlist, nthread=Threads.nthreads(), verbosity=0, eval_metric="logloss", params_xgb...);
+
+pred_xgb = XGBoost.predict(m_xgb, DMatrix(select(deval, feature_names)));
+@info extrema(pred_xgb)
+# (1.9394008f-6, 0.9999975f0)
+logloss_test = mean(-dtest.y .* log.(pred_xgb) .+ (dtest.y .- 1) .* log.(1 .- pred_xgb))
+@info "LogLoss - dtest" logloss_test
+error_test = 1 - mean(round.(Int, pred_xgb) .== dtest.y)
+@info "ERROR - xgb test" error_test
+# ┌ Info: LogLoss - dtest
+# └   logloss_test = 0.4710665675338929
+# ┌ Info: ERROR - xgb test
+# └   error_test = 0.22987999999999997